I am a student of netaji subhash engineering college. its our final year project

1. {
BRAIN-COMPUTER
INTERFACING TO DETECT
STRESS DURING MOTOR
IMAGERY TASKS
ABHISEK SENGUPTA Roll No.-02 WBUT Roll No.-10905514002
ARNAB BAIN Roll No.-11 WBUT Roll No.-10905514011
DEBOSHRUTI BANERJI Roll No.-15 WBUT Roll No.-10905514015
MAHIM MALLICK Roll No.-21 WBUT Roll No.-10905514021
PARAMITA DEY Roll No.-27 WBUT Roll No.-10905514027
TETASH BASU Roll No.-48 WBUT Roll No.-10905514048

2.  An Introduction to Brain-Computer
Interfacing
 Stress Detection during Motor-Imagery
Tasks
 Stress Detection of Vehicle Drivers during
Driving-A Case Study
 Conclusions and Future Scope
List Of Contents

3. Introduction to Brain
Computer Interface

5. Brain Map

6. Brain Imaging Techniques

7. Electroencephalography
Electroencephalogra
phy (EEG) is an
electrophysiological
monitoring method
to record the
spontaneous
electrical activity of
the brain over a
period of time. EEG
measures voltage
fluctuations resulting
from ionic
current within the
neurons of the brain.

8. Components of a BCI System

9. Stress and Motor Imaging
o Stress is primarily a
physical response, which
releases a complex mix of
hormones and chemicals
such as adrenaline,
cortisol and
norepinephrine to
prepare the body for
physical action.
o Motor imagery is a
cognitive process in
which a subject imagines
that he or she performs a
movement without
actually performing the
movement and without
even tensing the
muscles. It is a dynamic
state during which the
representation of a
specific motor action is
internally activated
without any motor
output

10. Decoding Stress and MI
Stress is detected
using eye blinks
and brain
activities from
EEG signals
The development of BCI
systems that can
effectively analyze
brain signals and
discriminate between
different MI tasks to
control neural
prostheses devices has
the potential to enhance
the quality of life for
people with severe
motor disabilities.

11. Features Used For Decoding MI
• Power Spectral Density : It
is a useful concept that
allows us to determine the
bandwidth of the system.
• The Fast Fourier
Transform (FFT) : It is a
useful scheme for
extracting frequency-
domain signal features.
• Wavelet transform (WT) : .
Its basic use includes time-
scale signal analysis, signal
decomposition and signal
compression. Other than
these Hjorth Parameters
and Kalman filter is also
used.

12. Classifiers Used For Decoding
Motor Imagery
 Generative or Informative
classifier - Discriminative classifier
 Static classifier - Dynamic
classifier
 Stable classifier - Unstable classifier
 Regularized classifier

13. Performance Analysis In MI Based
BCI Research
Fuzzy inference is a method that
interprets the values in the input
vector and, based on some set of
rules, assigns values to the
output vector, hence providing a
number of convenient ways to
create fuzzy sets.
A Membership function is a
curve that defines how each
point in the input space is
mapped to a membership value
or degree of membership
between 0 and 1. Here we are
using a Gaussian membership
function (gaussmf)
Gaussian membership curve

14. o An electrode placement
EEG system
o a 15 channels recording unit
(15 EEG electrodes are used )
o a video camera synchronized
with EEG recording
o a PC running a car racing
game for visual and
acoustic stimulation
o a steering, brake and
accelerator for the car
stimulator
Stress Detection Of Vehicle Drivers
During Driving-a Case Study
EEG recording of brain
activity
Car Simulation in PC

15. EEG signal acquisition during
the experiment

16. Screenshot of EEG signal showing mild stress
levels

17. Feature Extraction And Selection
Extracting Power
Spectral Density
Features
Extracting Fourier
Transform
Features
Extracting
Wavelet
Coefficient
Extracting
Hjorth
Parameters
Extracting
Kalman Filter
Coefficients

18. Power spectral density plots of 0.2 seconds EEG data

19. Fourier Transformed Signal
Raw EEG signal

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4 wavelets obtained for 1000x15 raw EEG data

21. Kalman filter analysis of EEG data

22. Classifier Validation And
Performance
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Hjorth Complexity Gaussian plot during alarmingly stressed video

23. Wavelet Coefficient Gaussian plot during alarmingly stressed video
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24. Kalman filter Gaussian plot during alarmingly stressed video
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25. Recognizing Stress Level
In the experiment, we collect and analyze EEG data during real-world driving
tasks to determine a driver's relative stress level among the 5 stress anchors.
EEG data were recorded continuously while drivers followed a set route
through open roads in the emulated driving scenario.
Minimum and Maximum Values of Features for Relaxed Video
1 2 3 4 5 6
Power Spectral Density 0.5313 0.4636 0.00239 0.39025 0.268 0.4901
Fourier Transform 0.7999 0.7556 0.6806 0.9688 0.7332 0.7336
Wavelet Coefficient 0.4931 0.4538 0.01 0.3166 0.3077 0.4642
Hjorth Complexity 0.9565 0.515 0.5379 0.7387 0.9328 0.97655
Hjorth Mobility 0.9498 0.9649 0.2703 0.9566 0.899 0.9957
Kalman 0.3657 0.3117 0.00001 0.7834 0.2716 0.8538

26. Similarly, minimum and maximum values are obtained for moderately
stressed and alarming stressed videos.
The triangular norm (tnorm) is used to calculate the membership values of
intersection of the fuzzy sets.
Stress Level Relax Situational Stress Alarming
Stress
tnorm
0.246555 0.49265 0.4141
tnorm for different stress levels
Since the maximum value of tnorm is in alarming stress set, hence the
subject is highly stressed and the car needs to be stopped for safety.

27. Future Research Directions
Experiments in this area have gradually shown promise. Scientists at Swiss
University are working with a car manufacturer Nissan to find out if they could
use brain signals to improve driving experience. The idea is that a computer on-
board the car could detect a driver’s intentions split seconds before they act by
reading their brain signals. The computer could then opt to intervene or assist
the driver, depending on external detection of other cars and objects around the
car. Brain measurements are used trying to understand what the driver is trying
to do. The brain signals are very good at detecting the whole environment
around the car and making the decisions themselves but the muscles react to
situations much slower as when we are driving we use both our hands and feet
making it complicated. So we are bad executors having response time slower.
The body controlling signals in our brain are still there and these brain signals
are utilized to make an automated car making response time faster and safer
than the driver would have made himself. But this doesn’t mean that the driver
is half-asleep and the computer does everything for him/her. The driver is still
kept active and the driving experience is improved.